Biogeography Based Optimization Technique Research Articles

A hybrid artificial intelligence approach for modeling the carbonation depth of sustainable concrete containing fly ash

One of the major challenges in the civil engineering sector is the durability of reinforced concrete structures against carbonation during the physico-chemical process of interaction of hydrated cementitious composites with carbon dioxide. This aggressive process causes carbon penetration into the reinforcement part, which affects the behavior of the structure during its lifetime due to corrosion risk. A countermeasure is using alternative cementitious materials to improve concrete texture and resist increased carbonation depth (CD). Considering that the CD test requires a long time and a skilled technician, this study strives to provide an alternative approach by moving from traditional laboratory-based methods towards artificial intelligence (AI) techniques for modeling the CD of sustainable concrete containing fly ash (CCFA). Despite the development of single AI models so far, it is undeniable that utilizing metaheuristic optimization techniques in the form of hybrid models can improve their performance. To this end, a new hybrid model from the integration of biogeography-based optimization (BBO) technique with artificial neural network (ANN) is developed for the first time to estimate the CD of CCFA. The error distribution results revealed that 59% of the ANN predictions had errors within the range of (− 1 mm, 1 mm], while the corresponding percentage for the ANN-BBO predictions was 70%, indicating an 11% reduction in the prediction errors by the proposed hybrid model. Furthermore, A10-index highlighted a performance improvement of 78% for the hybrid model, which met the closeness of the predicted values to the observed ones, so that the value of this index for models of ANN and ANN-BBO was 0.5019 and 0.8947, respectively. Analyzing the cross-validation confirmed the reliability and generalizability of the developed model. Also, the three most influential variables in estimating the CD were exposure time (27%), carbon dioxide concentration (22%), and water/binder (18%), respectively. Finally, the superiority of the ANN-BBO model was verified by comparing it with previous studies’ models.

Distance optimization and directional overcurrent relay coordination using edge-powered biogeography-genetic algorithms

The effective functioning and regulation of power systems crucially rely on the coordination of distance and directional overcurrent relays. Accurate fault detection and successful clearing sequences require support for each relay and the maintenance of the coordination time interval (CTI) between major distance relays, directional overcurrent relay support, and other relay zones. Efficiently initiating relays while adhering to complex coordination limitations poses a challenging task that demands innovative solutions. This study addresses the intricate problem of relay coordination by employing heuristic methods, specifically genetic algorithms (GA) and biogeography-based optimization (BBO), in both a 9-bus and 39-bus system. The primary objective is to determine the most efficient time setting factor (TSM) that minimizes the duration of relay operation. Additionally, the intelligent features of the overcurrent relay are carefully chosen to enhance the research's results. The integration of edge computing capabilities plays a significant role in advancing this coordination method. By incorporating advanced algorithms and communication technologies at the edge, the prompt activation of relays becomes possible, thereby meeting coordination demands. This study explores the combination of edge-based servers with genetic algorithms (GA) and biogeography-based optimization (BBO) techniques to enhance relay coordination. The findings indicate a notable enhancement compared to conventional approaches. However, comparative research suggests that BBO's performance is similar to GA, without a distinct advantage in achieving higher outcomes.

Adaptive multi-strategy particle swarm optimization for solving NP-hard optimization problems

Particle Swarm Optimization algorithm (PSO) has been widely utilized for addressing optimization problems due to its straightforward implementation and efficiency in tackling various test functions and engineering optimization problems. Nevertheless, PSO encounters issues like premature convergence and a lack of diversity, particularly when confronted with complex high-dimensional optimization tasks. In this study, we propose an enhanced version of the Island Model Particle Swarm Optimization (IMPSO), where island models are integrated into the PSO algorithm based on several migration strategies. The first contribution consists in applying a new selection and replacement strategies based on tabu search technique, while the second contribution consists in proposing a dynamic migration rate relying on the Biogeography-Based Optimization technique. To assess and validate the effectiveness of the proposed method, several unconstrained benchmark functions are applied. The obtained results confirm that the approach yield better performance than the old version of IMPSO for solving NP-hard optimization problems. Compared to the performance of other well-known evolutionary algorithms, the proposed approach is more efficient and effective.

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms.

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA)method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.

A LOCALLY AND GLOBALLY TUNED METAHEURISTIC OPTIMIZATION FOR OVERLAPPING COMMUNITY DETECTION

Many people use online social networks to share their opinions and information in this digital age. The number of people engaged and their dynamic nature pose a major challenge for social network analysis (SNA). Community detection is one of the most critical and fascinating issues in social network analysis. Researchers frequently employ node features and topological structures to recognize important and meaningful performance in order to locate non-overlapping communities. We introduce a locally and globally tuned multi-objective biogeography-based optimization (LGMBBO) technique in this research for detecting overlapping communities based on the number of connections and node similarity. Four real- world online social network datasets were used in the experiment to assess the quality of both overlapping and non-overlapping partitions. As a result, the model generates a set of solutions that have the best topological structure of a network with node properties. The suggested model will increase their productivity and enhance their abilities to identify significant and pertinent communities.

Numerical analysis and optimization of a novel photovoltaic thermal solar unit improved by Nano-PCM as an energy storage media and finned collector

An increment in temperature will deteriorate the efficiency of photovoltaic systems dramatically. The application of a cooling mechanism can reduce the operating temperature and perform a better operation. Therefore, in this research, a nanoparticle-based phase change material (PCM) layer is combined with finned collectors as the condenser for higher electricity generation in photovoltaic/thermal (PV/T) collectors. The temperature distribution of the PCM layer in both solid and fully melted phases are poked to identify the effect of the Nano-PCM layer on the mentioned system efficiency. Afterward, a big data collection is created and employed to introduce a deep learning model to tune and find the best network. Ultimately, the gilt-edged network is optimized using LINMAP, TOPSIS, as well as Shannon entropy decision makings and Gray Wolf Optimization (GWO), Bat algorithm (BA), Particle swarm optimization (PSO), and Biogeography-based optimization (BBO) techniques. The results indicated that the maximum thermal efficiency values are obtained at wind speed values less than 2 m/s and DNI higher than 950 W/m2. Considering their mentioned impacts on thermal efficiency, this variable of the renewable unit changes by 10–24%. Moreover, the optimal condition reveals the optimum melted PCM, the coolant outlet temperature, and the electrical efficiency of 6.497 kg, 37.72 °C, and 13.92%, respectively. It is concluded that compared with the traditional systems, enhancement in electrical efficiency can be achieved by Nano-PCM layer utilization as cooling media.

An approach to harmonic optimization for 25KVA auxiliary converter used in indian railway

As the Indian railway network expands, the demand for reliable and efficient power systems, especially auxiliary converters supporting various on-board systems, grows. By reducing power losses and fuel consumption, it is more crucial to design an energy-efficient and economic auxiliary converter for the railway system. This is partially accomplished by using a 25KVA auxiliary (Aux.) converter, which can accept input electrical energy from a 110V DC battery at Self-Generation (SG) coaches. Biogeography Based Optimization (BBO) method of harmonic elimination for a 25KVA converter is described in this study. The paper explores the 120-degree mode of conduction and compares the existing technique with a proposed 7-switching selective odd harmonic removal method. Results from Matlab simulations demonstrate the effectiveness of the BBO technique in reducing Total Harmonic Distortion (THD). The output voltage waveforms with and without BBO are compared, showcasing a significant improvement in harmonic suppression. The paper concludes by emphasizing the practical implementation of the optimized switching angles using an ATmega2560R3 microcontroller. The online application of stored switching angles on the microcontroller is calculated offline.

Optimal Design of Damping Control of Oscillations in Power System Using Power System Stabilizers with Novel Improved BBO Algorithm

Studies on power system stability are necessary for power network development & operation. Due to the great dimensionality and complexity of contemporary power systems, its significance has increased. The stability of an interconnected power system is seriously threatened by power system oscillation. Numerous strategies based on contemporary control theory, intelligent control, and optimization methods have been applied to the Power system stabilizers (PSSs) design problem recently. Each categorization contains a number of design techniques that increase the PSS's effectiveness and sturdiness in damping off low frequency vibrations. This work presents a new Modified and Improved Biogeography-Based Optimization (MIBBO) method to increase the optimization effectiveness of the usual Biogeography-Based Optimization (BBO) technique applied for the optimization of the parameters of the PSSs & Proportional Integral Derivative (PID) controller under the non-linear loading (NLL) conditions. The performance parameters which are obtained by the MIBBO based controller are compared with the results of normal BBO Method, Particle Swarm Optimization method (PSO) and Adaptation Law (AL) method. To justify the success and correctness of the proposed control approach, Matlab simulation results-based study of all the above-mentioned techniques is made and reported.

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications.

Several studies aimed at improving healthcare management have shown that the importance of healthcare has grown in recent years. In the healthcare industry, effective decision-making requires multicriteria group decision-making. Simultaneously, big data analytics could be used to help with disease detection and healthcare delivery. Only a few previous studies on large-scale group decision-making (LSDGM) in the big data-driven healthcare Industry 4.0 have focused on this topic. The goal of this work is to improve healthcare management decision-making by developing a new MapReduce-based LSDGM model (MR-LSDGM) for the healthcare Industry 4.0 context. Clustering decision-makers (DM), modelling DM preferences, and classification are the three stages of the MR-LSDGM technique. Furthermore, the DMs are subdivided using a novel biogeography-based optimization (BBO) technique combined with fuzzy C-means (FCM). The subgroup preferences are then modelled using the two-tuple fuzzy linguistic representation (2TFLR) technique. The final classification method also includes a feature extractor based on long short-term memory (LSTM) and a classifier based on an ideal extreme learning machine (ELM). MapReduce is a data management platform used to handle massive amounts of data. A thorough set of experimental analyses is carried out, and the results are analysed using a variety of metrics.

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

An efficient data replica placement mechanism using biogeography-based optimization technique in the fog computing environment

An efficient data replica placement mechanism using biogeography-based optimization technique in the fog computing environment

A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems

Cloud computing is one of the rapidly growing distributed computing technologies, and cloud-based applications have increased significantly in recent years. The amount of cloud resources and the number of cloud user are important metrics that affect the management of the cloud-based applications. Since the volume of traffic to cloud-based applications grows, the resource provisioning as one of challenging issues to serve time-varying and heterogeneous workloads in resource management scope to be considered. In this paper, we propose a workload clustering-based resource provisioning mechanism for executing cloud-based applications with heterogeneous workloads. Our proposed mechanism utilized biogeography-based optimization (BBO) technique with K-means clustering to classify the cloud workloads according to their quality of service (QoS) requirements. Besides, we used Bayesian learning technique to specify suitable resource provisioning actions to satisfy the QoS requirements of cloud-based applications. The simulation results obtained through simulation demonstrate that the proposed solution reduces the delay, SLA violation ratio, cost, and energy consumption compared with workload clustering-based resource provisioning mechanisms.

Optimal Sizing of Stand Alone Hybrid Renewable Energy System with Load Shifting

ABSTRACT In the present study, a model is proposed for the sizing optimization of a hybrid renewable energy system (HRES) based on local resource assessment and demand side management (DSM). It involves resource estimation for both micro hydro and solar photovoltaic systems. The sizing optimization of the HRES with or without load shifting based DSM is carried out using PSO (particle swarm optimization) and BBO (biogeography-based optimization) techniques. The purpose of DSM is to reduce the peak hourly power consumption. The cycle charging dispatch strategy has been used for sizing optimization of HRES. A comparative analysis has been made for PSO and BBO algorithms, by keeping energy index ratio at 1. Based on demand side management, the cost of energy, net present cost, renewable fraction, and emissions of carbon dioxide from diesel generators are also calculated. The results show that HRES with diesel generator (DG) of (10 kW), solar photovoltaic system (SPV) of (0.3 kW), and batteries of (300 Ah) each are most cost effective in the present scenario. This HRES combination consists of 4 units of DGs, 184 units SPV, and 44 units battery possesses 95.31% of renewable fraction during load shifting process with BBO algorithm. Finally, the proposed, model is validated with HOMER.

Distributed bandwidth selection approach for cooperative peer to peer multi-cloud platform

Network function virtualization (NFV) is a well-accepted software-defined network (SDN) architecture concept. NFV is severally used in cloud platform to enhance the utilization of available network resources. The concept behind NFV is a design of software process architecture with a bunch of collaborative resource utilization functions. The progression of platform providers builds a communication service plan with the help of virtual machines. We propose biogeography-based optimization (BBO) technique in order to enrich virtual network functions (VNFs) to utilize available bandwidth resource in an energy-efficient and cost-effective manner. The cooperative bandwidth sharing approach using BBO reduces delay mean at the time of supervision the impending requests in a multi-cloud environment. Experimental results validate that the utilization of available bandwidth has increased using the proposed scheme with respect to the single user-dedicated resource allocation and multi-user dedicated resource allocation. Delay means, as well as subscription cost, has reduced compared to the existing methods. The correctness of the investigational outcome has been established using an experimental setup along with the proposed algorithm.

Enhancing load frequency control of multi-area multi-sources power system including conventional and renewable energy units with nonlinearities

The foremost aims the Load Frequency Control (LFC) is to maintain the frequency at nominal value and minimize the unscheduled tie line power flow between different control areas. The penetration of renewable energy sources into the grid is a recent challenge to the power system operators due to their different modelling rather than conventional units. In this paper, enhancing load frequency control of multi-area multi-sources power system including renewable units system with nonlinearities is proposed using a new application of proportional–integral–derivative controller with proportional controller in the inner feedback loop, which is called as PID-P controller. To investigate the performance of the proposed controller, a thermal with reheater, hydro, wind and diesel power generation units with physical constraints such as governor dead band, generation rate constraint, time delay and boiler dynamics are considered. The proposed controller parameters are optimized using different heuristic optimization techniques such: Linearized Biogeography-Based Optimization technique, Biogeography-Based Optimization technique and Genetic Algorithm. The ability of the system to handle the large variation in load conditions, time delay, participation factors, and system parameters has been verified comprehensively.

Active and Reactive Load Ability Improvement using BBO Algorithm with FACTS Devices

Biogeography-based optimization (BBO) technique is used for the optimal positioning of FACTS devices under various loading cases in IEEE 14 bus system. In this paper, the effects of active and reactive loadings are studied and power flow analysis is performed for the proper placement of FACTS devices. The suggested method with active and reactive loading using FACTS devices is compared with Krill herd algorithm (KHA). It is found that the BBO algorithm improves real power loss, operating cost and system loadability significantly.

A case study on combined economic emission and load dispatch using biogeography based optimisation technique

Economic load dispatch (ELD) is economic scheduling of all generating units for variable load conditions of power system. At the same time, emission of pollutants from conventional thermal power plants need to be controlled for reducing air pollution. Therefore, optimisation of operating cost and emission of pollutants is required for the economic and safe power generation. This paper proposes optimisation of load scheduling for combined economic emission and load dispatch (CEELD) problems. Biogeography based optimisation (BBO) technique is employed to provide the lowest fuel cost at minimum emission of pollutants of all the generating units for a given load. The CEELD solution is obtained by applying BBO technique on a standard IEEE 30 bus - 6 generator system. All the parameters are evaluated in MATLAB for 50 and 100 population size at a given load. The results are compared with the existing methods to prove the efficacy of the proposed technique.

A case study on combined economic emission and load dispatch using biogeography based optimisation technique

A case study on combined economic emission and load dispatch using biogeography based optimisation technique

An empirical-based rainfall-runoff modelling using optimization technique

ABSTRACTThis study proposes a new hybrid biogeography-based optimization (BBO) technique to achieve a better balance between exploitation and exploration sides of BBO. The proposed hybrid metaheuristic algorithm, namely HBBPSGWO, enhances the exploration ability of BBO by combining it with the exploration side of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms. The proposed hybrid approach is integrated with two classical machine learning models, namely artificial neural network (ANN) and adaptive neuro-fuzzy system (ANFIS), for 1-day-ahead streamflow prediction in a catchment. Daily rainfall and discharge value from 1979 to 2015 of the catchment Fal at Tregony (United Kingdom) is used to validate the performance efficiency of the proposed hybrid algorithm, the HBBPSGWO along with ANN and ANFIS separately. The results demonstrate that the HBBPSGWO-ANN/HBBPSGWO-ANFIS improves the BBO convergence by avoid the trapping it into local minima and the performance has significantly improved compare to basic BBO-based ANN (BBO-ANN)/BBO-based ANFIS (BBO-ANFIS) and PSO-based ANN (PSO-ANN)/PSO-based ANFIS (PSO-ANFIS). In testing phase, root mean square error (RMSE) of HBBPSGWO-ANN is found lowest (0.901) compare to BBO-ANN (0.949) and PSO-ANN (0.926). Whereas in the case of integrated ANFIS, the RMSE of HBBPSGWO-ANFIS is also found minimum (0.741) compare to BBO-ANFIS (0.805) and PSO-ANFIS (0.828). The finding of this research concludes that the proposed hybrid metaheuristic algorithm has better capability to predict the daily streamflow. Moreover, the convergence of HBBPSGWO requires a smaller number of iterations to run in comparison to BBO and PSO.

A novel approach for rainfall-runoff modelling using a biogeography-based optimization technique

ABSTRACT This study adopted a novel biogeography-based optimization (BBO) using an adaptive neuro-fuzzy inference system (ANFIS) technique that is BBO-ANFIS, for one day ahead runoff forecasting. Further, to check robustness of BBO-ANFIS, a comparative study has been done with two well-known hybrid models genetic algorithm-based ANFIS (GA-ANFIS) and firefly-based ANFIS (FA-ANFIS). For model development, two input features (rainfall and runoff), i.e. historical daily accumulated mean rainfall at 1 km resolution and mean daily discharge of three river catchments (specifically, river Fal at Tragony station, river Seaton at Trebrownbridge station and river Kenwyn at Truro) are considered. For the performance evaluation of models, a range of model performance indicators (correlation coefficient (r), mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient) have been used. From results, it was found that the BBO-ANFIS model (r = 0.93, MAE = 0.29, NSE = 0.86 for Fal at Tragony, r = 0.96, MAE = 0.09, NSE = 0.92 for Seaton at Trebrownbridge and r = 0.93, MAE = 0.05, NSE = 0.87 for Kenwyn at Truro) has the best performance than the GA-ANFIS model (r = 91, MAE = 0.29, NSE = 0.82 for Fal at Tragony, r = 0.91, MAE = 0.10, NSE = 0.91 for Seaton at Trebrownbridge and r = 0.92, MAE = 0.06, NSE = 0.85 for Kenwyn at Truro) and the FA-ANFIS model (r = 0.91, MAE = 0.34, NSE = 0.82 for Fal at Tragony, r = 0.95, MAE = 0.10, NSE = 0.91 for Seaton at Trebrownbridge and r = 0.93, MAE = 0.06, NSE = 0.86 for Kenwyn at Truro) for rainfall-runoff (R-R) modelling, This study shows the sensitivity test of standalone machine learning model parameters using three metaheuristic techniques and identifies the best technique that can be efficiently used in hydrological modelling.